Detecting changes with multiple sensors has received significant attention in the literature. In many practical applications such as critical infrastructure monitoring and modeling of disease spread, a useful change propagation model is one where change eventually happens at all sensors, but where not all sensors witness change at the same time-instant. While the known change propagation dynamics was studied before, this paper studies a more general setting of unknown change propagation pattern (trajectory) in both centralized and decentralized settings with the goal of detecting the first time-instant any sensor witnesses a change. Using the dynamic programming framework, the optimal solution structures are derived and in the special rare-change regime, several more practical solutions are proposed. Under certain conditions, the first-order asymptotic optimality of a proposed algorithm called multichart test is shown as the false alarm probability vanishes. Moreover, low-complexity solutions are proposed based on online estimation of the change propagation pattern. Numerical studies illustrate that the proposed detectors offer near-optimal performance. Furthermore, in the decentralized setting, the level-crossing sampling is proposed for sampling and transmission of local statistics that significantly improves the detection performance using the same amount of communication resources compared to the conventional uniform-in-time sampling.


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    Title :

    Multisensor Sequential Change Detection With Unknown Change Propagation Pattern


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1317034 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English